Abstract
This study represents a first attempt to assess the detection capability of a fluorescence-enhanced optical imaging system as quantified by the Hotelling observer. The imaging system is simulated by the diffusion approximation of the time-dependent radiative transfer equation, which describes near infra-red (NIR) light propagation through clinically relevant tissue volumes. The random structures in the background are introduced using a lumpy-object model as a representation of anatomical structure as well as non-uniform distribution of disease markers. The systematic errors and noise associated with the actual experimental conditions are incorporated into the simulated boundary measurements to acquire imaging data sets. A large number of imaging data sets are considered in order to perform Hotelling observer studies. We find that the signal-to-noise ratio (SNR) of Hotelling observer (i) decreases as the strength of lumpy perturbations in the background increases, (ii) decreases as the target depth increases, and (iii) increases as excitation light leakage decreases, and reaches a maximum for filter optical density values of 5 or higher.